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Leaf Curling & Margin Scorch Diagnosis | 植物卷叶/焦边识别（干旱/病害）

Using agricultural cameras to capture high-resolution images of plant leaves, AI vision techniques detect leaf curling direction (up-curling or down-curling) and the distribution of leaf-margin scorch (old vs new leaves, tip vs margin). | 通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选），综合判断卷叶/焦边的主要原因（干旱胁迫、病害如白粉病/病毒病、药害、肥害等）。系统定期巡检，发现卷叶或焦边时自动分析原因，输出诊断及建议（如'叶片上卷、叶缘焦枯，土壤湿度偏低，可能干旱，建议灌溉'）。 Skill: Leaf Curling & Margin Scorch Diagnosis | 植物卷叶/焦边识别（干旱/病害） Owner: 18072937735 Summary: Using agricultural cameras to capture high-resolution images of plant leaves, AI vision techniques detect leaf curling direction (up-curling or down-curling) and the distribution of leaf-margin scorch (old vs new leaves, tip vs margin). | 通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选

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Rank

Safety

Downloads

1.8k

Updated

Oct 10, 2026

Version

1.0.10

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.8K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.8K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.10release · observed Aug 25, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-leaf-curling-scorch-diagnosis-analysis

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-18072937735-smyx-leaf-curling-scorch-diagnosis-analysis/snapshot"

Documentation

CLAWHUB

147,562 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "smyx-leaf-curling-scorch-diagnosis-analysis"
description: "Using agricultural cameras to capture high-resolution images of plant leaves, AI vision techniques detect leaf curling direction (up-curling or down-curling) and the distribution of leaf-margin scorch (old vs new leaves, tip vs margin). | 通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选），综合判断卷叶/焦边的主要原因（干旱胁迫、病害如白粉病/病毒病、药害、肥害等）。系统定期巡检，发现卷叶或焦边时自动分析原因，输出诊断及建议（如'叶片上卷、叶缘焦枯，土壤湿度偏低，可能干旱，建议灌溉'）。"
version: "1.0.12"
license: "MIT-0"
---

# 🍃 Leaf Curling & Margin Scorch Diagnosis | 植物卷叶/焦边识别（干旱/病害）
> **智能分析中枢** · 图片/视频智能分析 · 结构化报告 · 历史报告云端查询

---

## 🧭 技能概览 | Overview

| 模块 | 内容 |
|---|---|
| 🏷️ 技能名称 | **植物卷叶/焦边识别（干旱/病害）** |
| 🎯 核心目标 | 通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选），综合判断卷叶/焦边的主要原因（干旱胁迫、病害如白粉病/病毒病、药害、肥害等）。系统定期巡检，发现卷叶或焦边时自动分析原因，输出诊断及建议（如'叶片上卷、叶缘焦枯，土壤湿度偏低，可能干旱，建议灌溉'）。 |
| 🖼️ 输入类型 | 图片、视频、本地文件、网络 URL |
| 📝 输出能力 | 结构化分析报告、识别/监测结果、建议与报告链接 |
| 🧩 场景码 | `SMYX_LEAF_CURLING_SCORCH_DIAGNOSIS_ANALYSIS` |

Using agricultural cameras to capture high-resolution images of plant leaves, AI vision techniques detect leaf curling
direction (up-curling or down-curling) and the distribution of leaf-margin scorch (old vs new leaves, tip vs margin).
Combined with optional soil-moisture sensor data, the system jointly judges the most likely cause of curling/scorching (
drought stress, diseases such as powdery mildew or virus, pesticide damage, fertilizer burn, etc.). This helps farmers
quickly locate the problem and take targeted action. Application scenarios: open-field crops, greenhouse vegetables,
orchards. The system periodically inspects fields; when curling or scorching is detected it automatically analyzes the
cause and issues a diagnosis (e.g., 'leaves curled upward with margin scorch, soil moisture low — likely drought,
suggest irrigation'). Skill features: leaf curling and margin scorch are common but easy to misjudge because drought,
diseases and chemical damage share similar symptoms. AI-assisted visual diagnosis helps farmers respond correctly in
time and reduce losses. Can be integrated into agricultural IoT systems, UAV inspection platforms, or mobile apps.

通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选），综合判断卷叶/焦边的主要原因（干旱胁迫、病害如白粉病/病毒病、药害、肥害等）。该技能有助于农民快速定位问题，采取针对性措施。应用场景：大田作物、温室蔬菜、果园。系统定期巡检，发现卷叶或焦边时自动分析原因，输出诊断及建议（如'叶片上卷、叶缘焦枯，土壤湿度偏低，可能干旱，建议灌溉'
）。技能特点：卷叶和焦边是农民常遇到的问题，但干旱、病害、药害症状相似，易误判。通过AI视觉辅助诊断，可帮助农民早期采取正确措施，减少损失。该技能可集成到农业物联网系统、无人机巡检平台或手机APP中。

## 🤖 AI 角色 | AI Role
| 角色要点 | 说明 |
|---|---|
| 说明 1 | **假设你是一个专业的植物逆境诊断 AI。你的任务是分析植物叶片的图像，识别卷曲方向（上卷/下卷）、焦边分布（叶尖/叶缘、老叶/新叶），并可结合土壤湿度数据（若提供），判断引起卷叶/焦边的主要原因。不要提供具体的农药或肥料名称、剂量，仅输出基于视觉（及可选土壤湿度）的可能原因排序与方向性建议。 ** |

## 🎬 技能演示 | Skill Demo

[▶️ 点击查看技能使用介绍](https://lifeemergence.com/sample.html)

---

## 🎯 任务目标 | Goals
### 1. 🧩 技能用途

基于叶片高清图像（可选叠加土壤湿度等环境数据），识别卷曲方向与焦边分布特征，并给出干旱/病害/药害/肥害等原因的可能性排序

### 2. 🛠️ 能力范围

| 序号 | 具体

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-leaf-curling-scorch-diagnosis-analysis",
  "version": "1.0.10",
  "publishedAt": 1787685629927
}

references/api_doc.md

# API 接口文档

此处用于存放植物卷叶/焦边识别（干旱/病害）API 的接口文档，待后续补充。

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 响应格式：JSON

## 主要接口

1. `/web/health-analysis/v2/start-health-analysis` - 启动卷叶/焦边诊断任务
2. `/web/health-analysis/v2/get-health-analysis-result` - 获取分析结果与原因诊断
3. `/web/health-analysis/page-health-analysis-result` - 分页查询历史诊断记录
4. `/health/order/api/getReportDetailExport?id={id}` - 导出完整报告

## 场景代码

- `SMYX_LEAF_CURLING_MARGIN_SCORCH_DIAGNOSIS_ANALYSIS` - 植物卷叶/焦边识别（干旱/病害）

## 输入约束

- 推荐拍摄叶片整体形态（区分新叶 / 老叶）以及叶尖/叶缘特写
- 光照均匀、无明显阴影、聚焦清晰
- 可选附带传感器/环境数据：土壤湿度 %、空气湿度 %、近期施药/施肥记录

## 关键观测特征

- 卷曲方向：上卷（叶片向上反卷）/ 下卷（叶片向下内卷）
- 焦边分布：叶尖灼烧 / 叶缘焦枯 / 整叶干枯
- 分布部位：老叶 / 新叶 / 顶端嫩叶 / 全株
- 伴随特征：叶色变化（黄化/紫红）、白粉、坏死斑、水浸状斑

## 输出字段（参考）

- `curl_direction` - 卷曲方向（up_curl / down_curl / mixed / none）
- `scorch_pattern` - 焦边分布（tip_burn / margin_scorch / whole_leaf_dry）
- `affected_leaves` - 受害叶层（old_leaves / new_leaves / top_leaves / whole_plant）
- `likely_causes` - 可能原因排序（drought / disease_powdery_mildew / virus / pesticide_damage / fertilizer_burn / cold_stress 等）
- `confidence_top1` - 最可能原因的置信度
- `evidence_hints` - 关键视觉证据描述

> 仅输出基于视觉（及可选土壤湿度）的可能原因排序，不输出具体农药/肥料名称或剂量。

skills/smyx_analysis/references/api_doc.md

# API接口文档

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 错误码说明

| 错误码 | 说明       |
|-----|----------|
| 400 | 请求参数错误   |
| 401 | API密钥无效  |
| 403 | 权限不足     |
| 413 | 文件过大     |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误  |

scripts/config.yaml

{}

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